Task 1: Import data
library(readr) # to import tabular data (e.g. csv)
library(dplyr) # to manipulate (tabular) data
library(ggplot2) # to visualize data
Wildschwein_Daten <- read_csv(file = "wildschwein_BE.csv")
View(Wildschwein_Daten)Task 2: Explore Data
ggplot(Wildschwein_Daten, aes(Long, Lat, colour = TierID)) +
geom_point()
Input I: Handling spatial data (Tabelle in Spatial Object umwandeln)
library(sf)
wildschwein_BE_sf <- st_as_sf(Wildschwein_Daten, # Tabelle in Spatial Object umwandeln
coords = c("Long", "Lat"),
crs = 4326)
View(Wildschwein_Daten)
wildschwein_BE_sf## Simple feature collection with 51246 features and 4 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 7.019889 ymin: 46.97125 xmax: 7.112075 ymax: 47.01882
## Geodetic CRS: WGS 84
## # A tibble: 51,246 × 5
## TierID TierName CollarID DatetimeUTC geometry
## * <chr> <chr> <dbl> <dttm> <POINT [°]>
## 1 002A Sabi 12275 2014-08-22 21:00:12 (7.049618 46.99317)
## 2 002A Sabi 12275 2014-08-22 21:15:16 (7.049509 46.99416)
## 3 002A Sabi 12275 2014-08-22 21:30:43 (7.049406 46.99383)
## 4 002A Sabi 12275 2014-08-22 21:46:07 (7.049217 46.99375)
## 5 002A Sabi 12275 2014-08-22 22:00:22 (7.049359 46.99375)
## 6 002A Sabi 12275 2014-08-22 22:15:10 (7.049363 46.99382)
## 7 002A Sabi 12275 2014-08-22 22:30:13 (7.049326 46.99387)
## 8 002A Sabi 12275 2014-08-22 22:45:11 (7.049237 46.99395)
## 9 002A Sabi 12275 2014-08-22 23:00:27 (7.048383 46.99481)
## 10 002A Sabi 12275 2014-08-22 23:15:41 (7.049396 46.99373)
## # … with 51,236 more rowsis.data.frame(wildschwein_BE_sf) # -> [1] TRUE## [1] TRUE# subset rows
wildschwein_BE_sf[1:10,]## Simple feature collection with 10 features and 4 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 7.048383 ymin: 46.99317 xmax: 7.049618 ymax: 46.99481
## Geodetic CRS: WGS 84
## # A tibble: 10 × 5
## TierID TierName CollarID DatetimeUTC geometry
## <chr> <chr> <dbl> <dttm> <POINT [°]>
## 1 002A Sabi 12275 2014-08-22 21:00:12 (7.049618 46.99317)
## 2 002A Sabi 12275 2014-08-22 21:15:16 (7.049509 46.99416)
## 3 002A Sabi 12275 2014-08-22 21:30:43 (7.049406 46.99383)
## 4 002A Sabi 12275 2014-08-22 21:46:07 (7.049217 46.99375)
## 5 002A Sabi 12275 2014-08-22 22:00:22 (7.049359 46.99375)
## 6 002A Sabi 12275 2014-08-22 22:15:10 (7.049363 46.99382)
## 7 002A Sabi 12275 2014-08-22 22:30:13 (7.049326 46.99387)
## 8 002A Sabi 12275 2014-08-22 22:45:11 (7.049237 46.99395)
## 9 002A Sabi 12275 2014-08-22 23:00:27 (7.048383 46.99481)
## 10 002A Sabi 12275 2014-08-22 23:15:41 (7.049396 46.99373)wildschwein_BE_sf[wildschwein_BE_sf$TierName == "Sabi",]## Simple feature collection with 22746 features and 4 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 7.029718 ymin: 46.98947 xmax: 7.059671 ymax: 47.01454
## Geodetic CRS: WGS 84
## # A tibble: 22,746 × 5
## TierID TierName CollarID DatetimeUTC geometry
## <chr> <chr> <dbl> <dttm> <POINT [°]>
## 1 002A Sabi 12275 2014-08-22 21:00:12 (7.049618 46.99317)
## 2 002A Sabi 12275 2014-08-22 21:15:16 (7.049509 46.99416)
## 3 002A Sabi 12275 2014-08-22 21:30:43 (7.049406 46.99383)
## 4 002A Sabi 12275 2014-08-22 21:46:07 (7.049217 46.99375)
## 5 002A Sabi 12275 2014-08-22 22:00:22 (7.049359 46.99375)
## 6 002A Sabi 12275 2014-08-22 22:15:10 (7.049363 46.99382)
## 7 002A Sabi 12275 2014-08-22 22:30:13 (7.049326 46.99387)
## 8 002A Sabi 12275 2014-08-22 22:45:11 (7.049237 46.99395)
## 9 002A Sabi 12275 2014-08-22 23:00:27 (7.048383 46.99481)
## 10 002A Sabi 12275 2014-08-22 23:15:41 (7.049396 46.99373)
## # … with 22,736 more rows# subset colums
wildschwein_BE_sf[,2:3]## Simple feature collection with 51246 features and 2 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 7.019889 ymin: 46.97125 xmax: 7.112075 ymax: 47.01882
## Geodetic CRS: WGS 84
## # A tibble: 51,246 × 3
## TierName CollarID geometry
## <chr> <dbl> <POINT [°]>
## 1 Sabi 12275 (7.049618 46.99317)
## 2 Sabi 12275 (7.049509 46.99416)
## 3 Sabi 12275 (7.049406 46.99383)
## 4 Sabi 12275 (7.049217 46.99375)
## 5 Sabi 12275 (7.049359 46.99375)
## 6 Sabi 12275 (7.049363 46.99382)
## 7 Sabi 12275 (7.049326 46.99387)
## 8 Sabi 12275 (7.049237 46.99395)
## 9 Sabi 12275 (7.048383 46.99481)
## 10 Sabi 12275 (7.049396 46.99373)
## # … with 51,236 more rowswildschwein_BE <- st_as_sf(Wildschwein_Daten,
coords = c("Long", "Lat"),
crs = 4326)
rm(wildschwein_BE_sf)
# we can remove this sf object, since it just eats up our memoryTask 3: Project data from WGS84
st_transform(x = wildschwein_BE, crs = 2056)## Simple feature collection with 51246 features and 4 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 2568153 ymin: 1202306 xmax: 2575154 ymax: 1207609
## Projected CRS: CH1903+ / LV95
## # A tibble: 51,246 × 5
## TierID TierName CollarID DatetimeUTC geometry
## * <chr> <chr> <dbl> <dttm> <POINT [m]>
## 1 002A Sabi 12275 2014-08-22 21:00:12 (2570409 1204752)
## 2 002A Sabi 12275 2014-08-22 21:15:16 (2570402 1204863)
## 3 002A Sabi 12275 2014-08-22 21:30:43 (2570394 1204826)
## 4 002A Sabi 12275 2014-08-22 21:46:07 (2570379 1204817)
## 5 002A Sabi 12275 2014-08-22 22:00:22 (2570390 1204818)
## 6 002A Sabi 12275 2014-08-22 22:15:10 (2570390 1204825)
## 7 002A Sabi 12275 2014-08-22 22:30:13 (2570387 1204831)
## 8 002A Sabi 12275 2014-08-22 22:45:11 (2570381 1204840)
## 9 002A Sabi 12275 2014-08-22 23:00:27 (2570316 1204935)
## 10 002A Sabi 12275 2014-08-22 23:15:41 (2570393 1204815)
## # … with 51,236 more rowsInput II: Calculate Convex Hull (aus Punktwolke Polygone machen)
wildschwein_BE_grouped <- group_by(wildschwein_BE,TierID)
wildschwein_BE_grouped## Simple feature collection with 51246 features and 4 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 7.019889 ymin: 46.97125 xmax: 7.112075 ymax: 47.01882
## Geodetic CRS: WGS 84
## # A tibble: 51,246 × 5
## # Groups: TierID [3]
## TierID TierName CollarID DatetimeUTC geometry
## <chr> <chr> <dbl> <dttm> <POINT [°]>
## 1 002A Sabi 12275 2014-08-22 21:00:12 (7.049618 46.99317)
## 2 002A Sabi 12275 2014-08-22 21:15:16 (7.049509 46.99416)
## 3 002A Sabi 12275 2014-08-22 21:30:43 (7.049406 46.99383)
## 4 002A Sabi 12275 2014-08-22 21:46:07 (7.049217 46.99375)
## 5 002A Sabi 12275 2014-08-22 22:00:22 (7.049359 46.99375)
## 6 002A Sabi 12275 2014-08-22 22:15:10 (7.049363 46.99382)
## 7 002A Sabi 12275 2014-08-22 22:30:13 (7.049326 46.99387)
## 8 002A Sabi 12275 2014-08-22 22:45:11 (7.049237 46.99395)
## 9 002A Sabi 12275 2014-08-22 23:00:27 (7.048383 46.99481)
## 10 002A Sabi 12275 2014-08-22 23:15:41 (7.049396 46.99373)
## # … with 51,236 more rowswildschwein_BE_sample <- sample_n(wildschwein_BE_grouped, 2000)
wildschwein_BE_smry <- summarise(wildschwein_BE_sample)
wildschwein_BE_smry## Simple feature collection with 3 features and 1 field
## Geometry type: MULTIPOINT
## Dimension: XY
## Bounding box: xmin: 7.02001 ymin: 46.97184 xmax: 7.111014 ymax: 47.0188
## Geodetic CRS: WGS 84
## # A tibble: 3 × 2
## TierID geometry
## <chr> <MULTIPOINT [°]>
## 1 002A ((7.043728 46.99472), (7.043766 46.99469), (7.043629 46.99456), (7.043…
## 2 016A ((7.043067 46.99562), (7.043099 46.99563), (7.043112 46.99566), (7.043…
## 3 018A ((7.043432 46.99525), (7.043432 46.9953), (7.043325 46.99532), (7.0428…mcp <- st_convex_hull(wildschwein_BE_smry)
mcp## Simple feature collection with 3 features and 1 field
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 7.02001 ymin: 46.97184 xmax: 7.111014 ymax: 47.0188
## Geodetic CRS: WGS 84
## # A tibble: 3 × 2
## TierID geometry
## * <chr> <POLYGON [°]>
## 1 002A ((7.048278 46.99006, 7.044914 46.99263, 7.044254 46.99314, 7.033533 47…
## 2 016A ((7.074761 46.97184, 7.041213 46.98309, 7.034756 47.00236, 7.034853 47…
## 3 018A ((7.043432 46.99525, 7.02001 47.00084, 7.021248 47.01572, 7.03724 47.0…Task 4: Ploting spatial objects
plot(mcp)
ggplot(mcp, aes(colour = TierID)) +
geom_sf() +
coord_sf(datum =2056)
Input III: Importing raster data (import swisstopo-Map)
library(terra)
pk100_BE <- terra::rast("pk100_BE.tif")
pk100_BE## class : SpatRaster
## dimensions : 1821, 2321, 3 (nrow, ncol, nlyr)
## resolution : 5, 5 (x, y)
## extent : 2567000, 2578605, 1199996, 1209101 (xmin, xmax, ymin, ymax)
## coord. ref. : CH1903+ / LV95 (EPSG:2056)
## source : pk100_BE.tif
## names : pk1_1, pk1_2, pk1_3
## min values : 0, 0, 0
## max values : 255, 255, 255plot(pk100_BE)
plotRGB(pk100_BE)
Task 5: Adding a background map (Karte und Wildschwein-Daten zusammen plotten)
library(tmap)
tmap_mode("plot")
map1 <- tm_shape(shp = pk100_BE) +
tm_rgb() +
tm_shape(shp = mcp) +
tm_polygons("TierID", border.col = "black", lwd = 1.5, alpha = 0.4) + # alpha = transparency
tm_layout(legend.title.size = 1,
legend.text.size = 0.6,
legend.position = c("left","top"),
legend.bg.color = "white",
legend.bg.alpha = 1,
frame = F)
map1
Task 6: Create an interactive map
map1 <- tm_shape(shp = pk100_BE) +
tm_rgb() +
tm_shape(shp = mcp) +
tm_polygons("TierID", border.col = "black", lwd = 1.5, alpha = 0.4) + # alpha = transparency
tm_compass(type = "arrow", position = c(0.23,0.021), bg.color = "white", bg.alpha = 0.75, size = 1.8) +
tm_scale_bar(breaks = c(0, 0.5, 1, 1.5, 2), text.size = 0.75, position = c("left", "bottom"), bg.color = "white", bg.alpha = 0.75) +
tm_credits("Scale: 1:25'000 \nData source: swisstopo, ZHAW \nAuthor: Jonas M. Windisch", fontface = "italic", bg.color = "white", bg.alpha = 0.75, width = 1, position = c(0.73,0.02)) +
tm_logo(file = "zhaw_lsfm_iunr_schwarz.png", position = c(0.79,0.86)) +
tm_layout(main.title = " Movement Patterns of Wild-Boar (Sus Scrofa) in the Seeland District (2014-2015)",
main.title.size = 1,
legend.title.size = 1,
legend.text.size = 0.6,
legend.position = c("left","top"),
legend.bg.color = "white",
legend.bg.alpha = 1,
frame = F)
map1
tmap_mode("view")
map1 + tm_minimap()tmap_mode("plot")
tmap_save(tm = map1, filename = "Aufgabe1.pdf", width = 17, height = 14, units = "cm", dpi = 5000)